Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design with feature thickness control, include: a three-dimensional modeling program configured to provide voxelized thinning including preparing a voxelized sheet and line skeleton for a three-dimensional shape of a three-dimensional model, and defining thickness values for the three-dimensional shape using the voxelized sheet and line skeleton. The three-dimensional modeling program can be an architecture, engineering and/or construction program (e.g., building information management program), a product design and/or manufacturing program (e.g., a CAM program), and/or a media and/or entertainment production program (e.g., an animation production program).
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
One embodiment sets forth a technique for generating prompts for generative artificial intelligence (AI) models. According to some embodiments, the technique can include the steps of receiving an initial prompt comprising natural language; generating a plurality of parameterized prompt attributes based on the initial prompt; generating a plurality of UI elements based on the parameterized prompt attributes; receiving user input modifying one or more of the UI elements; and generating an updated prompt based on the initial prompt and the user input.
Various embodiments set forth techniques for generating computer-aided design (CAD) models that include generating a plurality of geometric prompts based on a plurality of inputs, wherein the plurality of inputs indicate at least one geometric value by which at least one CAD model to be generated is to be constrained, and the at least one geometric value is characterized by at least one mathematical inequality, executing a trained machine learning model on the geometric prompts to generate CAD data, and generating the at least one CAD model based on the CAD data, wherein the at least one CAD model is constrained in accordance with the at least one geometric value. Advantageously, the disclosed techniques can substantially facilitate the overall process of designing CAD objects and CAD models of differing levels of complexity, thereby increasing the accessibility of CAD software and applications to a wider array of users.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
4.
DYNAMIC USER INTERFACES FOR MODIFYING GENERATIVE AI PROMPTS
One embodiment sets forth a technique for generating prompts for generative artificial intelligence (AI) models. According to some embodiments, the technique can include the steps of receiving an initial prompt comprising natural language; generating a plurality of parameterized prompt attributes based on the initial prompt; generating a plurality of UI elements based on the parameterized prompt attributes; receiving user input modifying one or more of the UI elements; and generating an updated prompt based on the initial prompt and the user input.
A computer-implemented method includes receiving, by an AI system, a request to generate an interactive design API, the request comprising unstructured data indicative of an objective to be achieved by using the interactive design API; generating, by the AI system, structured data based on the unstructured data; generating, by the AI system, the interactive design API based on the structured data; and publishing, by the AI system, the interactive design API. Another computer-implemented method includes receiving, by an AI system, multimodal input associated with an objective to be achieved by using an executable tool; determining, by the AI system, based on evaluating the multimodal input, at least one solver to be included in the executable tool for achieving the objective; generating, by the AI system, the executable tool comprising the at least one solver; and publishing, by the AI system, the executable tool.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
G06N 3/006 - Vie artificielle, c.-à-d. agencements informatiques simulant la vie fondés sur des formes de vie individuelles ou collectives simulées et virtuelles, p. ex. simulations sociales ou optimisation par essaims particulaires [PSO]
G06F 30/13 - Conception architecturale, p. ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software for analyzing, managing and visualizing time series data, map-based information, and spatial temporal analytics for use in managing, operating or evaluating the performance of water management systems, assets, and networks. Software as a service (SaaS) for analyzing, managing and visualizing time series data, map-based information, and spatial temporal analytics for use in managing, operating or evaluating the performance of water management systems, assets, and networks; hosting of software as a service (SaaS); online provision of web-based applications and software (non-downloadable) for analyzing, managing and visualizing time series data, map-based information, and spatial temporal analytics for use in managing, operating or evaluating the performance of water management systems, assets, and networks; cloud computing; rental of computer software, including charging a fee for access to a non-downloadable computer software service for analyzing, managing and visualizing time series data, map-based information, and spatial temporal analytics for use in managing, operating or evaluating the performance of water management systems, assets, and networks.
7.
AUGMENTING SPEECH TRANSCRIPTS OF VIRTUAL REALITY RECORDINGS
One embodiment sets forth a technique for generating an augmented transcript of a single-user virtual reality (VR) session. According to some embodiments, the technique includes the steps of identifying a first referring expression in a text transcript of the VR session performed by a user in a VR environment; analyzing one or more non-verbal behaviors of the user during the VR session to determine a first VR object in the VR environment associated with the first referring expression; and specifying a first name of the first VR object in the text transcript to generate the augmented transcript. Another embodiment sets forth a technique for generating an augmented transcript of a two-user virtual reality (VR) session.
A computer-implemented method includes receiving, by an AI system, a request to generate an interactive design API, the request comprising unstructured data indicative of an objective to be achieved by using the interactive design API; generating, by the AI system, structured data based on the unstructured data; generating, by the AI system, the interactive design API based on the structured data; and publishing, by the AI system, the interactive design API. Another computer-implemented method includes receiving, by an AI system, multimodal input associated with an objective to be achieved by using an executable tool; determining, by the AI system, based on evaluating the multimodal input, at least one solver to be included in the executable tool for achieving the objective; generating, by the AI system, the executable tool comprising the at least one solver; and publishing, by the AI system, the executable tool.
A computer-implemented method includes receiving, by an AI system, a request to generate an interactive design API, the request comprising unstructured data indicative of an objective to be achieved by using the interactive design API; generating, by the AI system, structured data based on the unstructured data; generating, by the AI system, the interactive design API based on the structured data; and publishing, by the AI system, the interactive design API. Another computer-implemented method includes receiving, by an AI system, multimodal input associated with an objective to be achieved by using an executable tool; determining, by the AI system, based on evaluating the multimodal input, at least one solver to be included in the executable tool for achieving the objective; generating, by the AI system, the executable tool comprising the at least one solver; and publishing, by the AI system, the executable tool.
In various embodiments a computer-implemented method for navigating design workspaces comprises acquiring, via an audio sensor, a speech input signal of a user, detecting, in the speech input signal, a navigation command portion to move at least a portion of a design object within a design workspace, in response to detecting the navigation command portion, automatically generating a graphical overlay over at least a portion of the design workspace, where the graphical overlay includes a plurality of identifiers, detecting a subsequent navigation command identifying a selection of a first identifier included in the plurality of identifiers, and moving the portion of the design object to a location associated with the first identifier.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SaaS) featuring integrated physical
asset management, operational, and planning software that
integrates digital model data with multi-functional data,
such as construction, real-time operations, business,
building automation, facility management and internet of
things (IoT) systems data, to create a digital twin or
virtual replica of a building, structure, or other physical
asset to improve operational efficiency in the fields of
construction, architecture, engineering, real estate,
facility, and structural management.
12.
PARETO FRONT GENERATION USING DESIGN GENERATIVE MODELS
One embodiment sets forth a technique for generating design Pareto fronts that includes receiving requirement data; generating, based on a first target requirement included in the requirement data, one or more first perturbed target requirement values generating, based on the requirement data and the one or more first perturbed target requirement values and using one or more machine learning models, one or more first sampled designs; generating, based on the one or more first sampled designs and using a simulator, a first design Pareto front; and performing at least one action based on the first design Pareto front.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
13.
TRAINING DESIGN GENERATIVE MODELS USING SIMULATION
One embodiment sets forth a technique for training a generative model to generate one or more designs includes receiving requirement data, performing, based on the requirement data, one or more operations to generate one or more design trajectories using a first trained machine learning model, where the first trained machine learning model is trained to generate one or more first designs, performing, based on the requirement data and the one or more design trajectories, one or more training operations to retrain the first trained machine learning model to generate a second trained machine learning model, and generating, based on one or more first requirements and using the second trained machine learning model, one or more first predicted designs.
Methods, systems, and apparatus, including medium-encoded computer program products for rendering objects for display include: maintaining, by a computer having a display device and a local memory, a data structure in the local memory; receiving, by the computer, a request to render a current frame for display on the display device; sequentially determining, by the computer, nodes of a first set of current nodes that are associated with the current frame and that are already included in the data structure; in response to the determining, updating the data structure; and rendering, by the computer, the current frame for display on the display device based on the updated data structure.
One embodiment sets forth a technique for training a generative model to generate one or more designs includes receiving requirement data, performing, based on the requirement data, one or more operations to generate one or more design trajectories using a first trained machine learning model, where the first trained machine learning model is trained to generate one or more first designs, performing, based on the requirement data and the one or more design trajectories, one or more training operations to retrain the first trained machine learning model to generate a second trained machine learning model, and generating, based on one or more first requirements and using the second trained machine learning model, one or more first predicted designs.
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
Techniques for multi-region constraint enforcement in deep neural networks include receiving training data, training a DNN without constraints on the training data to generate a base model, assigning a unique sign pattern for each of a plurality of disjoint convex regions, enforcing the unique sign pattern for each of the plurality of disjoint convex regions by adjusting parameters of the base model to ensure each disjoint convex region in the plurality of disjoint convex regions lies in a unique affine polytope, fine-tuning the base model with updated parameters, and enforcing an affine constraint on each of the plurality of disjoint convex regions of the DNN.
One embodiment sets forth a technique for training a generative model to generate one or more designs that includes receiving first requirement data; performing, using a simulator and a first trained machine learning model, one or more operations to generate one or more requirement-satisfying designs based on the first requirement data, where the first trained machine learning model is trained to generate one or more first designs based on the first requirement data; performing, based on the one or more requirement-satisfying designs and the first requirement data, one or more training operations to retrain the first trained machine learning model to generate a second trained machine learning model; and generating, based on one or more first requirements and using the second trained machine learning model, one or more first predicted designs.
One embodiment sets forth a technique for training a generative model for to generate one or more designs includes receiving requirement data, performing, using a simulator and a first trained machine learning model, one or more operations to generate one or more preferred designs and one or more rejected designs, where the first trained machine learning model is trained to generate one or more first designs based on the requirement data, performing, based on the requirement data, the one or more preferred designs, and the one or more rejected designs, one or more training operations to retrain the first trained machine learning model to generate a second trained machine learning model, and generating, based on one or more first requirements and using the second trained machine learning model, one or more first predicted designs.
Methods, systems, and apparatus, including medium-encoded computer program products for rendering objects for display include: obtaining, by a computer having a display device and a local memory, a bounding volume hierarchy including nodes corresponding to a plurality of objects in a 3D model of an environment; by the computer, a queue data structure in the local memory of the computer; traversing, by the computer, the bounding volume hierarchy to add one or more nodes of the nodes of the bounding volume hierarchy to the queue data structure; and rendering, by the computer, the objects of the one or more nodes on the display device.
Methods, systems, and apparatus, including medium-encoded computer program products, for generating CAD action sequence using machine learning, include: receiving a representation of a three-dimensional object to be modified; generating a state encoding for the object from the representation of the three-dimensional object; autoregressively predicting a sequence of a plurality of subsequent modification actions to be performed on the object including: repeatedly performing a masked attention process using a current sequence conditioned on the generated state encoding of the object, wherein the masked attention process uses: (i) a causal self-attention mask that allows the masked attention process to attend to encodings of prior actions, and (ii) a causal cross-attention mask that allows the masked attention process to attend to one or more state encodings of the object; and displaying a final sequence of predicted modification actions for the object in a user interface of a computer modeling program.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
21.
DESIGN PARETO FRONT GENERATION USING DESIGN GENERATIVE MODELS
One embodiment sets forth a technique for generating design Pareto fronts that includes receiving requirement data; generating, based on a first target requirement included in the requirement data, one or more first perturbed target requirement values; generating, based on the requirement data and the one or more first perturbed target requirement values and using one or more machine learning models, one or more first sampled designs; generating, based on the one or more first sampled designs and using a simulator, a first design Pareto front; and performing at least one action based on the first design Pareto front.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
22.
TRAINING DESIGN GENERATIVE MODELS USING SIMULATION
One embodiment sets forth a technique for training a generative model to generate one or more designs that includes receiving first requirement data; performing, using a simulator and a first trained machine learning model, one or more operations to generate one or more requirement-satisfying designs based on the first requirement data, where the first trained machine learning model is trained to generate one or more first designs based on the first requirement data; performing, based on the one or more requirement-satisfying designs and the first requirement data, one or more training operations to retrain the first trained machine learning model to generate a second trained machine learning model; and generating, based on one or more first requirements and using the second trained machine learning model, one or more first predicted designs.
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
Various embodiments include a computer-implemented method for generating designs, including generating a proxy object within an intermediate design based on a first prompt, generating a first resolved object within the intermediate design based on a second prompt, determining that the proxy object can be replaced with a second resolved object based on a design context associated with the intermediate design, and generating the second resolved object based on the first resolved object and the design context.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06T 19/20 - Édition d'images tridimensionnelles [3D], p. ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
24.
NATURAL LANGUAGE TOOLS FOR PRECISE CONTROL AND NAVIGATION
In various embodiments, a computer-implemented method for navigating design workspaces comprises acquiring, via an audio sensor, a speech input signal of a user, detecting, in the speech input signal, an initial lengthened command portion for a lengthened command to move at least a portion of a design object within a design workspace, detecting a lengthened command portion in a subsequent input signal of the user, in response to detecting the lengthened command portion, executing the lengthened command, where executing the lengthened command continues as the user continues to provide the subsequent input signal, and terminating execution of the lengthened command upon detecting an end of the lengthened command portion.
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
25.
NATURAL LANGUAGE TOOLS FOR PRECISE CONTROL AND NAVIGATION
In various embodiments a computer-implemented method for navigating design workspaces comprises acquiring, via an audio sensor, a speech input signal of a user, detecting, in the speech input signal, a navigation command portion to move at least a portion of a design object within a design workspace, in response to detecting the navigation command portion, automatically generating a graphical overlay over at least a portion of the design workspace, where the graphical overlay includes a plurality of identifiers, detecting a subsequent navigation command identifying a selection of a first identifier included in the plurality of identifiers, and moving the portion of the design object to a location associated with the first identifier.
G06F 3/04845 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs pour la transformation d’images, p. ex. glissement, rotation, agrandissement ou changement de couleur
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
26.
GENERATING VISUALIZATIONS OF CONSTRAINED CAD DRAWINGS USING GEOMETRIC VARIATIONS
A computer-implemented method for visualizing behaviors of constrained computer-aided design (CAD) drawings includes receiving a CAD drawing that includes a plurality of geometric elements; generating, via a constraint solver, a plurality of constrained versions of the CAD drawing, wherein each constrained version of the CAD drawing included in the plurality of constrained versions of the CAD drawing includes a unique combination of one or more geometric constraints; generating a plurality of geometric element variations for a particular constrained version of the CAD drawing included in the plurality of constrained versions of the CAD drawing; and generating and displaying a user interface that includes the plurality of geometric element variations for the particular constrained version of the CAD drawing.
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
G06T 19/20 - Édition d'images tridimensionnelles [3D], p. ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
27.
GENERATING CLUSTERS OF CONSTRAINED CAD DRAWINGS BASED ON GEOMETRIC REGULARITY
A computer-implemented method for grouping constrained computer-aided design (CAD) drawings includes receiving a plurality of constrained CAD drawings; generating a plurality of geometric variations, wherein each geometric variation in the plurality of geometric variations is based on one of the constrained CAD drawings in the plurality of constrained CAD drawings; generating, for each geometric variation included in the plurality of geometric variations, a regularity graph that represents geometric regularities between a plurality of geometric elements included in the geometric variation; sorting the geometric variations based on the regularity graphs to generate a plurality of filtered constrained CAD drawings; and displaying, via a user interface, the plurality of filtered constrained CAD drawings.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
28.
GENERATING CAD ACTION SEQUENCE BY MACHINE LEARNING
Methods, systems, and apparatus, including medium-encoded computer program products, for generating CAD action sequence using machine learning, include: receiving a representation of a three-dimensional object to be modified; generating a state encoding for the object from the representation of the three-dimensional object; autoregressively predicting a sequence of a plurality of subsequent modification actions to be performed on the object including: repeatedly performing a masked attention process using a current sequence conditioned on the generated state encoding of the object, wherein the masked attention process uses: (i) a causal self-attention mask that allows the masked attention process to attend to encodings of prior actions, and (ii) a causal cross-attention mask that allows the masked attention process to attend to one or more state encodings of the object; and displaying a final sequence of predicted modification actions for the object in a user interface of a computer modeling program.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
A computer-implemented method for compressing a long prompt for an artificial intelligence (AI) model, the method comprising generating an original similarity score for the long prompt and a long-prompt output that is generated by the AI model based on the long prompt, computing a set of feature scores for a set of features of the long prompt based, at least in part, on the original similarity score, determining a set of salient features included in the set of features based on the set of feature scores, and generating a short prompt based on the set of salient features, wherein the short prompt comprises a compressed version of the long prompt for inputting to the AI model to generate a short-prompt output.
One embodiment sets forth a technique for classifying generative AI prompts. According to some embodiments, the technique includes the steps of receiving a generative AI prompt; generating a deterministic score for a portion of the generative AI prompt; assigning a classification to the portion of the generative AI prompt based on the deterministic score; and rendering, via a user interface, the portion of the generative AI prompt with a graphical classification feature that indicates the classification of the portion of the generative AI prompt.
A computer-implemented method for grouping constrained computer-aided design (CAD) drawings includes receiving a plurality of constrained CAD drawings; generating a plurality of geometric variations, wherein each geometric variation in the plurality of geometric variations is based on one of the constrained CAD drawings in the plurality of constrained CAD drawings; generating, for each geometric variation included in the plurality of geometric variations, a regularity graph that represents geometric regularities between a plurality of geometric elements included in the geometric variation; sorting the geometric variations based on the regularity graphs to generate a plurality of filtered constrained CAD drawings; and displaying, via a user interface, the plurality of filtered constrained CAD drawings.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
Various embodiments include a computer-implemented method for generating designs, including determining a design context based on a first user input, retrieving a set of design prompts based on the design context, determining a first design prompt included in the set of design prompts based on a second user input, retrieving a set of design options based on the first design prompt, determining a first design option included in the set of design options based on third user input, and incorporating the first design option into the design context.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
Various embodiments include a computer-implemented method for generating designs, including generating a proxy object within an intermediate design based on a first prompt, generating a first resolved object within the intermediate design based on a second prompt, determining that the proxy object can be replaced with a second resolved object based on a design context associated with the intermediate design, and generating the second resolved object based on the first resolved object and the design context.
Various embodiments include a computer-implemented method for generating designs, including, receiving a first user input describing a design, generating a first set of design options based on the first user input, generating a first clarifying question related to a first attribute of the design, receiving a second user input describing the first attribute of the design, generating a second set of design options based on the first clarifying question and the second user input, generating a refined design based on the second set of design options.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 111/02 - CAO dans un environnement de réseau, p. ex. CAO coopérative ou simulation distribuée
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SAAS) featuring software for digital
animation, three-dimensional character performance and
animation, graphics, special effects, visual effects, video
and computer games for use in the field of entertainment;
software as a service (SAAS) featuring software in the
nature of cloud-based integrated workflows and workflow
environments, data and technology services between computer
software programs for creating, collaborating, developing,
rendering, manipulating, executing, viewing, and displaying
digital images and photographs, digital animation,
three-dimensional character performance and animation,
graphics, special effects, visual effects, video and
computer games for use in the field of entertainment;
software as a service (SAAS) services featuring software
using artificial intelligence for enabling users to animate,
light, and compose CG (computer generated) 3D characters
into videos and 3D environments; providing on-line
non-downloadable software using artificial intelligence for
enabling users to animate, light, and compose CG (computer
generated) 3D characters into videos and 3D environments via
a website; providing temporary use of on-line
non-downloadable cloud computing software using artificial
intelligence for enabling users to animate, light, and
compose CG (computer generated) 3D characters into videos
and 3D environments.
36.
EXPLORATORY CREATION OF CHARACTER CAST VISUALS USING GENERATIVE AI
One embodiment sets forth a computer-implemented method for generating images. The method can include generating, via a generative artificial intelligence (AI) model, a first set of image variants of a first fictional character based at least on one of a sketch of the first fictional character or a textual description of the first fictional character; associating the first set of image variants with a first logical group based on a first group theme and a first group prompt; generating a first character card comprising the first set of image variants associated with the first logical group; and displaying, via a user interface, the first character card to enable selection of at least one of the first set of image variants based on at least the first group theme.
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
A63F 13/58 - Commande des personnages ou des objets du jeu en fonction de la progression du jeu en calculant l’état des personnages du jeu, p. ex. niveau de vigueur, de force, de motivation ou d’énergie
37.
NATURAL LANGUAGE TOOLS FOR PRECISE CONTROL AND NAVIGATION
In various embodiments, a computer-implemented method for navigating design workspaces comprises acquiring, via an audio sensor, a speech input signal of a user, detecting, in the speech input signal, an initial lengthened command portion for a lengthened command to move at least a portion of a design object within a design workspace, detecting a lengthened command portion in a subsequent input signal of the user, in response to detecting the lengthened command portion, executing the lengthened command, where executing the lengthened command continues as the user continues to provide the subsequent input signal, and terminating execution of the lengthened command upon detecting an end of the lengthened command portion.
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la paroleSélection d'unités de reconnaissance
38.
GENERATING VISUALIZATIONS OF CONSTRAINED CAD DRAWINGS USING GEOMETRIC VARIATIONS
A computer-implemented method for visualizing behaviors of constrained computer-aided design (CAD) drawings includes receiving a CAD drawing that includes a plurality of geometric elements; generating, via a constraint solver, a plurality of constrained versions of the CAD drawing, wherein each constrained version of the CAD drawing included in the plurality of constrained versions of the CAD drawing includes a unique combination of one or more geometric constraints; generating a plurality of geometric element variations for a particular constrained version of the CAD drawing included in the plurality of constrained versions of the CAD drawing; and generating and displaying a user interface that includes the plurality of geometric element variations for the particular constrained version of the CAD drawing.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
Various embodiments include a computer-implemented method for embedding a watermark into a design, including identifying a first value corresponding to a first attribute of the design, determining that the first value includes a subset of data that is inaccessible to a user, and embedding the watermark into the design by modifying the first value to indicate that the design was created using a machine learning model.
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Production of visual effects for creating imagery and for
video productions. Providing temporary use of online non-downloadable
text-to-animation, text-to image, text-to-camera, and
text-to-video generator software using artificial
intelligence (AI); software as a service (SAAS) services
featuring software using artificial intelligence for
enabling users to create digital content, namely digital
characters and digital environments, for visual effects
related to replicating and reproducing camera movement, for
editing footage and character performances, for
segmentation, and for the animation of faces and
environments; providing online non-downloadable software
using artificial intelligence for enabling users to create
digital content, namely digital characters and digital
environments, for visual effects related to replicating and
reproducing camera movement, for editing footage and
character performances, for segmentation, and for the
animation of faces and environments; providing temporary use
of online non-downloadable cloud computing software using
artificial intelligence for enabling users to create digital
content, namely digital characters and digital environments,
for visual effects related to replicating and reproducing
camera movement, for editing footage and character
performances, for segmentation, and for the animation of
faces and environments; design of visual effects for video
productions.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SAAS) featuring software for computer
graphics; software as a service (SAAS) featuring software
for three dimensional modelling; software as a service
(SAAS) featuring software for making, editing and
manipulating computer graphics model; software as a service
(SAAS) featuring software for the traversal of 3D computer
graphics models for a variety of fields; software as a
service (SAAS) services featuring software for the creation,
design, visualization, simulation, modeling, analysis,
collaboration, implementation, and storage of building,
construction, infrastructure, pre-construction, operations,
and environment data for use in building, construction,
engineering, and infrastructure projects; providing online
non-downloadable computer software for the creation, design,
visualization, simulation, modeling, analysis,
collaboration, implementation, and storage of building
schematics, blueprints, and plans, construction projects,
infrastructure information, pre-construction projects,
construction operations, and environmental data for use in
building, construction, engineering and infrastructure
projects; software as a service (SAAS) services featuring
computer software for design, component and material
estimation, quality and safety management, data management,
risk management, facilities management, analytics
prediction, and for managing, communicating, collaborating,
and conducting analysis, all related to construction
projects; providing online non-downloadable computer
software for design, component and material estimation,
quality and safety management, data management, risk
management, facilities management, analytics prediction, and
for managing, communicating, collaborating, and conducting
analysis, all related to construction projects; computer
services, namely, cloud-hosting provider services of digital
content on the internet concerning the development of
construction projects; providing online non-downloadable
software that enables users to manage the production and
publication of digital images and related digital content
concerning the development of construction projects;
developing and managing application software for delivery of
digital content provided for construction project management
for use on wireless mobile devices; database development in
the field of digital content provided for construction
project management for use on wireless mobile devices;
digital enhancement and manipulation of construction project
images by means of computerized software for use in the
field of construction management; software as a service
(SAAS) featuring software for automating, connecting, and
coordinating of pre-construction review and building
information modeling, for quantification, clash detection,
model review, simulations and analysis of models for use in
the field of construction management.
42.
Attention-Based Learning For Fluid State Interpolation and Editing in a Time-Continuous Framework
A method and system provide the ability to interpolate fluids. At least two keyframes are produced, for a physics based fluid simulation. The keyframes are within a continuous-time framework and separated by a defined interval. Each keyframe includes one or more fluid elements having a corresponding state. Data is prepared utilizing a pre-trained transformer-based network by: (i) handling a tokenization process in a physics-adapted context; and (ii) generating temporal embeddings for states of the one or more fluid elements. Based on the prepared data, a time-continuous density is prepared for substeps between the two keyframes using a density network.
G06F 30/28 - Optimisation, vérification ou simulation de l’objet conçu utilisant la dynamique des fluides, p. ex. les équations de Navier-Stokes ou la dynamique des fluides numérique [DFN]
43.
AUTOMATED ANNOTATIONS FOR COMPUTER-AIDED DESIGN (CAD) DRAWINGS
A method and system provide for annotating a computer-aided design (CAD) drawing. Existing drawings are obtained and include annotations and geometries that serve as hosts. A machine learning (ML) model is trained on the extracted geometries and annotations. A new drawing is obtained. First user input selecting a first geometry in the new drawing is received and the first annotation is created. The ML model generates potential new hosts and annotations. The potential new annotations are displayed in the new drawing and second user input selects one of the potential new annotations to utilize as one or more new annotations.
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
Providing business information concerning innovation and
customer service in the field of software development;
providing information in the fields of employee inclusion
and belonging and leadership development; providing
information in the fields of career development, personnel
recruitment, corporate culture, and corporate
sustainability. Providing online educational information concerning
innovation and customer service in the field of software
development via a website; providing online non-downloadable
electronic publications in the nature of guides and
brochures in the fields of career development, personnel
recruitment, corporate culture, and corporate
sustainability, employee inclusion and belonging, leadership
development, and innovation and customer service in the
field of software development; providing online educational
information in the fields of career development, personnel
recruitment, corporate culture, and corporate sustainability
via a website; providing online educational information in
the fields of employee inclusion and belonging and
leadership development via a website.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software for project management for use in the fields of entertainment production and media production and video game development; Downloadable software for media production and entertainment production project management for scheduling, reviewing, managing, and analyzing project resources, assets, tasks, documents, timelines, versions, and schedules; Downloadable software for media production and entertainment production project management for collaboration, representation and sharing of information, interactive discussions to other users, process automation, integration with third-party content creation software and production pipelines, production pipeline automation, and uploading and transferring files for media and entertainment production projects Software as a service (saas) services featuring software for project management for use in the fields of entertainment production and media production and video game development; software as a service (saas) services featuring software for media production and entertainment production project management for scheduling, reviewing, managing, and analyzing project resources, assets, tasks, documents, timelines, versions, and schedules; software as a service (saas) services featuring software for media production and entertainment production project management for collaboration, representation and sharing of information, interactive discussions to other users, process automation, integration with third-party content creation software and production pipelines, production pipeline automation, and uploading and transferring files for media and entertainment production projects
46.
GENERATIVE REAL-TIME FEEDBACK FOR USER-GENERATED DESIGNS
In various embodiments, a computer-implemented method for generating feedback for a design comprises generating a captured portion of a design space, where the captured portion includes at least a portion of one or more design objects included in the design space, generating a feedback generation prompt that includes the captured portion, inputting the feedback generation prompt into a trained machine learning (ML) model for execution, receiving a set of feedback content items generated by the trained ML model in response to the feedback generation prompt, and providing the set of feedback content items in the design space.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
47.
NODE BASED STATE MACHINE FOR CONTROLLING THREE-DIMENSIONAL (3D) APPLICATION
A method and system provide for operating a three-dimensional (3D) computer animation and visual effects application (3D application). Execution of a multi-step 3D animation, modeling, or visual effects operation is initialized. Progression of the operation is controlled via a node-based state machine having a plurality of stage nodes daisy-chained via defined dependencies, each stage node including a condition attribute. Upon activation of a first stage node, a script associated with the first stage node that configures behavior of the 3D application is activated. Application events are monitored. A determination is made that the condition attribute of the first stage node has been satisfied. Upon satisfaction of the condition attribute, execution transitions to a subsequent stage node and a corresponding script is executed that modifies a scene state or animation state.
One embodiment sets forth a technique for generating an augmented transcript of a single-user virtual reality (VR) session. According to some embodiments, the technique includes the steps of identifying a first referring expression in a text transcript of the VR session performed by a user in a VR environment; analyzing one or more non-verbal behaviors of the user during the VR session to determine a first VR object in the VR environment associated with the first referring expression; and specifying a first name of the first VR object in the text transcript to generate the augmented transcript. Another embodiment sets forth a technique for generating an augmented transcript of a two-user virtual reality (VR) session.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Software as a service (SaaS) featuring integrated physical asset management, operational, and planning software that integrates digital model data with multi-functional data, such as construction, real-time operations, business, building automation, facility management and internet of things (IoT) systems data, to create a digital twin or virtual replica of a building, structure, or other physical asset to improve operational efficiency in the fields of construction, architecture, engineering, real estate, facility, and structural management.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SaaS) featuring integrated physical asset management, operational, and planning software that integrates digital model data with multi-functional data, such as construction, real-time operations, business, building automation, facility management and internet of things (IoT) systems data, to create a digital twin or virtual replica of a building, structure, or other physical asset to improve operational efficiency in the fields of construction, architecture, engineering, real estate, facility, and structural management.
51.
ENHANCING PHYSICAL REASONING IN VISION-LANGUAGE MODELS USING PROCEDURAL SYNTHETIC DATA GENERATION
One embodiment sets forth a technique for fine-tuning a machine learning model to perform physical reasoning. According to some embodiments, the method can include the steps of obtaining simulation annotations that describe interactions among simulated objects within a physics-based environment and one or more question templates, each question template defining a different parameterized reasoning query; generating, based on the simulation annotations and the one or more question templates, a plurality of question-answer pairs that represent physical reasoning examples; formatting the question-answer pairs into natural-language data compatible with the machine learning model; and fine-tuning the machine learning model based on the natural-language data.
One embodiment sets forth a technique for generating training data for physical reasoning models. According to some embodiments, the technique can include the steps of obtaining simulation annotations generated by a physics-based simulation environment for a plurality of simulated scenes; generating a plurality of scene descriptions based on the simulation annotations; generating a training dataset by combining the plurality of scene descriptions with corresponding visual data depicting the plurality of simulated scenes; and training at least one physical reasoning model using the training dataset to generate at least one trained physical reasoning model.
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
A method and system provide for automating drawing. A drawing of two or more entities is obtained and a resolution is determined. Based on the resolution, the drawing is quantized into a cell map in which spatial information is lost. The cell map is a collection of multiple cells stored in a contiguous memory. Each of the multiple cells is quantized geometry data and domain specific information. The cell map is utilized to automate a drawing process workflow faster and more efficiently than relying on conventional geometry data structures.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
54.
GENERATING VIRTUAL OBJECTS USING AUTOREGRESSIVE MODELS AND MULTI-SCALE TOKENIZATION
The disclosed method for generating virtual objects includes generating, based on object data, compressed object data, performing, based on the object data and scales, operations to train a first untrained machine learning model to generate a first trained machine learning model comprising a trained codebook and a trained decoder, wherein the first trained machine learning model is trained to generate a reconstruction of the compressed object data, generating, based on the compressed object data and the scales and using the first trained machine learning model, token maps data, performing, based on the token maps data and conditions, operations to train a second untrained machine learning model to generate a second trained machine learning model comprising a trained autoregressive model, wherein the second trained machine learning model is trained to generate predicted token maps, and generating, based on the scales, conditions, and using both trained models, a virtual object.
In various embodiments, a computer-implemented method for determining compositions of buildings includes receiving a plurality of building images associated with a building, generating a conditional image based on the plurality of building images, generating a plurality of tokens characterizing the building, providing the conditional image and the plurality of tokens to a neural network to cause the neural network to generate an output, generating, via a generative artificial intelligence (AI) model, a structural floorplan of the building based on the output and the plurality of tokens, and determining a composition of the building based on the structural floorplan.
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
A method and system provide the ability to generate and use synthetic data to extract elements from a floor plan drawing. A room layout is generated. Room descriptions are used to generate and place synthetic instances of symbol elements in each room. A floor plan drawing is obtained and pre-processed to determine a drawing area. Based on the synthetic data symbols in the floor plan drawing are detected. Based on the detected symbols, building information model (BIM) elements are fetched and placed in the floor plan drawing.
G06F 30/13 - Conception architecturale, p. ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
G06F 16/21 - Conception, administration ou maintenance des bases de données
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
One embodiment of the present invention sets forth a technique for providing awareness of privacy-related activities. The technique includes determining a privacy level associated with a user of an extended reality environment. The technique also includes presenting, using an internal display of a headset, one or more internal indicators identifying a location of a bystander located in a real-world environment, wherein a level of detail of each internal indicator is based on the privacy level associated with the user. The technique further includes presenting, using an external display, one or more external indicators that include a monitoring indicator representing being captured by the headset and presented to the user via the headset, and further include a user activity indicator representing one or more activities of the user, wherein a level of detail of the user activity indicator is based on the privacy level associated with the user.
One embodiment of a computer-implemented method includes receiving the plurality of design constraints defining properties of a machine assembly, receiving a first selection of a first part in a user interface, the first part selected from a virtual parts inventory, identifying, using a generative machine learning model, a second part from the virtual parts inventory connectable to the first part based on the plurality of design constraints and a second selection of a first location within the first part, and displaying the first part and the second part in a user interface, wherein the first part and the second part comprise a first portion of the machine assembly.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
59.
NARRATIVE SPACE GENERATION FOR AI-BRIDGED INTERACTIVE STORYTELLING
A computer-implemented method includes receiving a text-based story; generating, via a generative artificial intelligence (AI) model, a narrative outline based on the text-based story; generating one or more narrative instances based on the narrative outline; simulating a performance of each of the one or more narrative instances; and displaying, via at least one user interface, a visual representation of the performance of each of the one or more narrative instances.
A63F 13/45 - Commande de la progression du jeu vidéo
A63F 13/67 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu en s’adaptant à ou par apprentissage des actions de joueurs, p. ex. modification du niveau de compétences ou stockage de séquences de combats réussies en vue de leur réutilisation
60.
TECHNIQUES FOR IMPLEMENTING AN AUTO-COMPLETION SYSTEM FOR MECHANICAL ASSEMBLY DESIGNS
One embodiment sets forth techniques for providing part suggestions and placements within mechanical assembly designs. According to some embodiments, the techniques can include generating, via at least one generative artificial intelligence (AI) model and based on a mechanical assembly design, a ranked list of suggested parts that are compatible to be incorporated into the mechanical assembly design; receiving a first selection of a part from among the ranked list of suggested parts; generating, via the at least one generative AI model, a plurality of suggested placement locations for the part based on the mechanical assembly design and the part; receiving a second selection of a placement location from among the plurality of suggested placement locations; generating an updated mechanical assembly design that incorporates the part based on the placement location; and rendering at least one user interface (UI) that displays at least a portion of the updated mechanical assembly design.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
61.
VISUALIZATION OF ASSEMBLY DESIGNS GENERATED USING MACHINE LEARNING MODELS
One embodiment of a computer-implemented method includes receiving the plurality of design constraints in a user interface, the plurality of design constraints defining properties of a machine assembly. The method further includes generating, using a generative machine learning model, a plurality of machine assemblies based on the plurality of design constraints, and calculating a degree to which a machine assembly from the plurality of machine assemblies conforms to the plurality of design constraints. The method also includes displaying a degree to which the machine assembly conforms to at least one design constraint from the plurality of design constraints in the user interface.
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
62.
AI-ASSISTED DESIGN OF SPACES AND TRANSIENT ATMOSPHERES
A computer-implemented method for generating three-dimensional (3D) models of environments includes receiving an input; generating, based on the input and using at least one generative artificial intelligence (AI) model, one or more transient primitives for a three-dimensional (3D) model of an environment; applying the one or more transient primitives to the 3D model of the environment to generate a modified 3D model of the environment; and displaying the modified 3D model of the environment via at least one user interface.
One embodiment of a computer-implemented method includes receiving the plurality of design constraints in a user interface, the plurality of design constraints defining properties of a machine assembly. The method further includes generating, using a generative machine learning model, a plurality of machine assemblies based on the plurality of design constraints, and calculating a degree to which a machine assembly from the plurality of machine assemblies conforms to the plurality of design constraints. The method also includes displaying a degree to which the machine assembly conforms to at least one design constraint from the plurality of design constraints in the user interface.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
One embodiment sets forth a computer-implemented method for performing operations associated with modifying product assemblies. The computer-implemented method includes receiving a request for modifying a rendered product assembly; performing, in response to the request, at least one modification to the rendered product assembly; concurrently displaying a modified product assembly that reflects the at least one modification to the rendered product assembly; and concurrently updating a graphic display of one or more attributes of one or more components of the modified product assembly to enable evaluation of an effect of the at least one modification to the rendered product assembly.
Methods, systems, and apparatus, including medium-encoded computer program products, including: receiving, by a shape modeling computer program, a selection of first geometry defined in a data structure used by the computer program to represent a three-dimensional model of an object and an indication of an amount of complexity reduction, The computer program produces a second geometry defined in the data structure based on the indication of the amount of indicated complexity reduction and taking into account local shape curvature for the first geometry, where the second geometry replaces the first geometry in representing the three-dimensional model of the object. The computer program provides the three-dimensional model of the object, with the second geometry included in the three-dimensional model, for use in manufacturing a physical structure corresponding to the object using one or more computer-controlled manufacturing systems, or for use in displaying the object on a display screen.
One embodiment sets forth techniques for providing part suggestions and placements within mechanical assembly designs. According to some embodiments, the techniques can include generating, via at least one generative artificial intelligence (AI) model and based on a mechanical assembly design, a ranked list of suggested parts that are compatible to be incorporated into the mechanical assembly design; receiving a first selection of a part from among the ranked list of suggested parts; generating, via the at least one generative AI model, a plurality of suggested placement locations for the part based on the mechanical assembly design and the part; receiving a second selection of a placement location from among the plurality of suggested placement locations; generating an updated mechanical assembly design that incorporates the part based on the placement location; and rendering at least one user interface (UI) that displays at least a portion of the updated mechanical assembly design.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06F 111/20 - CAO de configuration, p. ex. conception par assemblage ou positionnement de modules sélectionnés à partir de bibliothèques de modules préconçus
67.
NARRATIVE SPACE GENERATION FOR AI-BRIDGED INTERACTIVE STORYTELLING
A computer-implemented method includes receiving a text-based story; generating, via a generative artificial intelligence (AI) model, a narrative outline based on the text-based story; generating one or more narrative instances based on the narrative outline; simulating a performance of each of the one or more narrative instances; and displaying, via at least one user interface, a visual representation of the performance of each of the one or more narrative instances.
A63F 13/65 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu automatiquement par des dispositifs ou des serveurs de jeu, à partir de données provenant du monde réel, p. ex. les mesures en direct dans les compétitions de course réelles
68.
ITERATIVE DESIGN OF MACHINE ASSEMBLIES USING MACHINE LEARNING MODELS
One embodiment of a computer-implemented method includes receiving the plurality of design constraints defining properties of a machine assembly, receiving a first selection of a first part in a user interface, the first part selected from a virtual parts inventory, identifying, using a generative machine learning model, a second part from the virtual parts inventory connectable to the first part based on the plurality of design constraints and a second selection of a first location within the first part, and displaying the first part and the second part in a user interface, wherein the first part and the second part comprise a first portion of the machine assembly.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Production of visual effects for creating imagery and for video productions.
(2) Providing temporary use of online non-downloadable text-to-animation, text-to image, text-to-camera, and text-to-video generator software using artificial intelligence (AI); software as a service (SAAS) services featuring software using artificial intelligence for enabling users to create digital content, namely digital characters and digital environments, for visual effects related to replicating and reproducing camera movement, for editing footage and character performances, for segmentation, and for the animation of faces and environments; providing online non-downloadable software using artificial intelligence for enabling users to create digital content, namely digital characters and digital environments, for visual effects related to replicating and reproducing camera movement, for editing footage and character performances, for segmentation, and for the animation of faces and environments; providing temporary use of online non-downloadable cloud computing software using artificial intelligence for enabling users to create digital content, namely digital characters and digital environments, for visual effects related to replicating and reproducing camera movement, for editing footage and character performances, for segmentation, and for the animation of faces and environments; design of visual effects for video productions.
70.
BUILD-ANGLE FILTERING DURING SYNTHESIZING OF THREE-DIMENSIONAL MODELS OF PHYSICAL OBJECTS FOR ADDITIVE MANUFACTURING PROCESSES
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using three-dimensional model synthesis processes. A method includes: obtaining a build angle, a manufacturing direction, a design space, and one or more design criteria for use in a shape synthesis process, the build angle and the manufacturing direction being for an additive manufacturing process; performing the shape synthesis process including applying a build-angle filter to a boundary-based computer-data representation of an intermediate version of the shape of the modeled object during multiple iterations, including removing a portion of material from the intermediate version of the shape in accordance with the build angle and the manufacturing direction to make the intermediate version of the shape self-supporting in a vicinity of the portion of material; and providing the shape of the modeled object for use in manufacturing using the additive manufacturing process.
One embodiment sets forth a technique for completing computerized representations of physical structures using knowledge graphs. According to some embodiments, the technique includes the steps of generating a knowledge graph that characterizes relationships between various features of computerized representations of physical structures; training one or more machine learning models based on the knowledge graph; receiving a request for adding a selected feature to a computerized representation of a physical structure; generating predicted feature data using the one or more trained machine learning models and the request; and causing the predicted feature data to be rendered at a graphical user interface (GUI) to suggest a predicted feature to be included with the selected feature. Another embodiment sets forth a technique for training machine learning models using knowledge graphs associated with computerized representations of physical structures.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 30/13 - Conception architecturale, p. ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
72.
TILE LOADING STRATEGY FOR RENDERING LARGE THREE-DIMENSIONAL MODELS
Methods, systems, and apparatus, including medium-encoded computer program products, for 3D model rendering, include: obtaining, by a computer having a display device and local memory, a three-dimensional scene description data structure encoding location information in a three-dimensional model of an environment, wherein the three-dimensional model is stored on a remote computer system, and the location information comprises bounding volumes for objects in the three-dimensional model; downloading, by the computer and from the remote computer system, a portion of the objects to the local memory; and rendering, by the computer, the portion of the objects along with one or more three-dimensional tiles representing a portion of the three-dimensional model in which at least one of the objects of the three-dimensional model that has not been downloaded is located, in accordance with the three-dimensional scene description data structure.
A method (400) for incorporation material into building assembly designs can include the steps of receiving (402) first input data that defines a building assembly design, wherein the building assembly design includes at least one material layer; generating (404), via at least one generative artificial intelligence (Al) model, an assembly graph based on the input data, wherein the assembly graph describes at least one relationship associated with the at least one material layer; receiving (406) second input data that describes at least one constraint for generating an updated building assembly design; generating (408), via the at least one generative Al model, the updated building assembly design based on the at least one constraint and the assembly graph; and displaying (410), via at least one user interface, information associated with the updated building assembly design.
G06F 30/13 - Conception architecturale, p. ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
One embodiment sets forth a technique for incorporation material into building assembly designs. According to some embodiments, the technique can include the steps of receiving first input data that defines a building assembly design, wherein the building assembly design includes at least one material layer; generating, via at least one generative artificial intelligence (AI) model, an assembly graph based on the input data, wherein the assembly graph describes at least one relationship associated with the at least one material layer; receiving second input data that describes at least one constraint for generating an updated building assembly design; generating, via the at least one generative AI model, the updated building assembly design based on the at least one constraint and the assembly graph; and displaying, via at least one user interface, information associated with the updated building assembly design.
G06F 30/13 - Conception architecturale, p. ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06F 111/02 - CAO dans un environnement de réseau, p. ex. CAO coopérative ou simulation distribuée
75.
VISION FOUNDATION MODELS FOR LARGE SCALE POINT CLOUD ANALYSIS, SEGMENTATION, AND CLASSIFICATION
A method and system provide the ability to segment a first point cloud. The first point cloud is rendered into multiple two-dimensional (2D) images. The images are segmented to generate a semantic segmentation mask. The images are then backprojected into a 3D classified point cloud. The classified point cloud is segmented into geometric segments and voting is performed for each segment to determine the majority classification and reassign minority classifications. A final point cloud is then exported as a segmented classified point cloud.
G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques
G01S 17/894 - Imagerie 3D avec mesure simultanée du temps de vol sur une matrice 2D de pixels récepteurs, p. ex. caméras à temps de vol ou lidar flash
G06F 30/13 - Conception architecturale, p. ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
76.
SERVICE ACCOUNTS FOR AUTHENTICATION AND ACCESS CONTROL IN DISTRIBUTED COMPUTING SYSTEMS
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using service accounts for authentication and access control in a distributed computing platform. A distributed computing platform having a plurality of computers configured to execute instructions implementing a plurality of software subsystems includes an identity management subsystem, one or more platform tools, and an application, wherein the identity management subsystem is configured to maintain user accounts for human users to access the one or more platform tools, and wherein the identity management subsystem is configured to maintain service accounts for non-human entities in the platform including assigning a service account to the application.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
Methods, systems, and apparatus, including medium-encoded computer program products for generating a three-dimensional mesh of an object include: receiving a text description that specifies a target three-dimensional geometry of a surface of the object; generating, using a diffusion model and based on the text description, a two-dimensional geometry image that encodes the target three-dimensional geometry into a two-dimensional array of pixels, wherein each pixel of the two-dimensional array of pixels represents a vertex of a plurality of vertices of the three-dimensional mesh that is to be generated for the object; and generating, based on the two-dimensional geometry image, the three-dimensional mesh of the object for rendering at a display of a physical device, wherein the three-dimensional mesh comprises the plurality of vertices defining a shape of the object, each vertex of the plurality of vertices having a corresponding location that is determined based on the two-dimensional geometry image.
A computer-implemented technique for characterizing performance of mechanical systems includes receiving sensor data that includes one or more measurements of a mechanical system; extracting, via a trained machine learning model, one or more values from the sensor data based on documentation associated with the mechanical system; and computing, via the trained machine learning model and based on the one or more values, one or more performance characteristics of the mechanical system.
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
79.
TECHNIQUES FOR USING KNOWLEDGE GRAPHS TO AUTOMATICALLY COMPLETE DRAFT STRUCTURAL DESIGNS
One embodiment sets forth a technique for completing computerized representations of physical structures using knowledge graphs. According to some embodiments, the technique includes the steps of generating a knowledge graph that characterizes relationships between various features of computerized representations of physical structures; training one or more machine learning models based on the knowledge graph; receiving a request for adding a selected feature to a computerized representation of a physical structure; generating predicted feature data using the one or more trained machine learning models and the request; and causing the predicted feature data to be rendered at a graphical user interface (GUI) to suggest a predicted feature to be included with the selected feature. Another embodiment sets forth a technique for training machine learning models using knowledge graphs associated with computerized representations of physical structures.
G06F 30/13 - Conception architecturale, p. ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
80.
TECHNIQUES FOR USING KNOWLEDGE GRAPHS TO AUTOMATICALLY COMPLETE DRAFT STRUCTURAL DESIGNS
One embodiment sets forth a technique for completing computerized representations of physical structures using knowledge graphs. According to some embodiments, the technique includes the steps of generating a knowledge graph that characterizes relationships between various features of computerized representations of physical structures; training one or more machine learning models based on the knowledge graph; receiving a request for adding a selected feature to a computerized representation of a physical structure; generating predicted feature data using the one or more trained machine learning models and the request; and causing the predicted feature data to be rendered at a graphical user interface (GUI) to suggest a predicted feature to be included with the selected feature. Another embodiment sets forth a technique for training machine learning models using knowledge graphs associated with computerized representations of physical structures.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
G06F 111/02 - CAO dans un environnement de réseau, p. ex. CAO coopérative ou simulation distribuée
G06N 5/022 - Ingénierie de la connaissanceAcquisition de la connaissance
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using hybrid additive and subtractive manufacturing include, in one aspect, a method including: obtaining data for 3D geometry of a part; simulating at least a portion of a manufacturing process that includes adding first material in a first stage and removing second material in a second, subsequent stage, where the second material includes a portion of the first material, removing the second material includes blending between the material added in the first and second stages, and thermal effects of adding and removing the material in the first and second stages is simulated; and adjusting an amount of the portion based on results of the simulating to prevent deviation of the part from the three dimensional geometry that results in not enough material being available for the blending.
A method and system provide the ability to align a railway track. Survey representing the track are obtained. A centerline of the railway track and curvatures are autonomously computed based on the survey points. Geometry is detected based on the curvature plot and provides for geometry types including a tangent, a spiral, and a curve. An alignment is displayed and includes a curvature plot that includes the detected geometry with each geometry type displayed in a visually distinguishable manner. Parameters of the detected geometry are changed and the alignment is dynamically updated. The alignment is then exported to a physical machine that aligns the railway track consistent with the alignment.
E01B 35/10 - Applications des appareils ou dispositifs de mesure à la construction des voies pour mesurer les irrégularités dans le sens longitudinal pour l'alignement
E01B 29/16 - Transport, pose, enlèvement, ou remplacement des railsDéplacement des rails placés sur traverses dans la voie
83.
TECHNIQUES FOR GENERATING VIRTUAL OBJECTS USING LATENT DIFFUSION MODELS
One embodiment sets forth a technique for generating virtual objects. According to some embodiments, the technique includes generating, based on object data, compressed object data; performing, based on the compressed object data, one or more operations to train an untrained machine learning model to generate a trained machine learning model that comprises a trained decoder, where the trained machine learning model is trained to generate a reconstruction of the compressed object data; and generating, based on one or more conditions, a predicted virtual object using a trained diffusion model and the trained decoder.
One embodiment sets forth a technique for generating virtual objects. According to some embodiments, the technique includes generating, based on object data, compressed object data; performing, based on the compressed object data, one or more operations to train an untrained machine learning model to generate a trained machine learning model that comprises a trained decoder, where the trained machine learning model is trained to generate a reconstruction of the compressed object data; and generating, based on one or more conditions, a predicted virtual object using a trained diffusion model and the trained decoder.
Generative constraining and dimensioning of CAD sketches receiving an input sketch, the input sketch including geometric entities; processing the input sketch to determine one or more properties of each of the geometric entities, the one or more properties of a first geometric entity including a plurality of points along the first geometric entity, the points capturing a shape of the first geometric entity; generating embedded tokens from the properties of each of the geometric entities; generating contextualized geometry and constraint embeddings from the embedded tokens using a first transformer; gathering the contextualized geometry and constraint embeddings to generate a plurality of gathered constraints; processing the gathered constraints using a second transformer to generate pointers; and processing the pointers and the geometry and constraint embeddings using a pointer network to autoregressively generate a constraint sequence.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
A method and system provide the ability to process source computer instructions. The source computer instructions are obtained and include input statements that consist of two functions that consume one or more arguments. A legal configuration of the functions and arguments is determined. A first function can be evaluated to yield a non-variable value, and a second function cannot be evaluated to yield a non-variable value. The input statements are compiled into executable code using the determined legal configuration such that during compilation, the first function is executed, and instructions are emitted to execute the second function at an indeterminate time.
09 - Appareils et instruments scientifiques et électriques
Produits et services
Downloadable software using artificial intelligence (AI) and
machine learning for enabling users to create, design,
optimize, model, simulate, visualize, collaborate on and
engineer products; downloadable software for computer aided
design (CAD); downloadable software utilizing artificial
intelligence (AI), machine learning and simulation results
to accelerate and optimize product design; downloadable
software using predictive models, machine learning and
generative design to create, design, optimize, model,
simulate, visualize, collaborate on and engineer products.
88.
GENERATIVE CONSTRAINING AND DIMENSIONING OF COMPUTER-AIDED DESIGN SKETCHES
Generative constraining and dimensioning of CAD sketches receiving an input sketch, the input sketch including geometric entities; processing the input sketch to determine one or more properties of each of the geometric entities, the one or more properties of a first geometric entity including a plurality of points along the first geometric entity, the points capturing a shape of the first geometric entity; generating embedded tokens from the properties of each of the geometric entities; generating contextualized geometry and constraint embeddings from the embedded tokens using a first transformer; gathering the contextualized geometry and constraint embeddings to generate a plurality of gathered constraints; processing the gathered constraints using a second transformer to generate pointers; and processing the pointers and the geometry and constraint embeddings using a pointer network to autoregressively generate a constraint sequence.
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
Generative constraining and dimensioning of CAD sketches includes receiving training data comprising a plurality of training data elements, each training data element comprising an input sketch and a ground truth constraint sequence, selecting a first training data element from the plurality of training data elements, generating a variable length prompt from the first training data element, presenting the variable length prompt to a constraint generation model to generate a first constraint sequence, generating a loss based on the first constraint sequence, and updating the constraint generation model based on the loss.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
90.
GENERATIVE CONSTRAINING AND DIMENSIONING OF COMPUTER-AIDED DESIGN SKETCHES
Generative constraining and dimensioning of CAD sketches includes generating one or more candidate constraint sequences using a constraint generation model, generating one or more quality scores for each of the candidate constraint sequences, and performing alignment training on the constraint generation model based on the one or more quality scores and the one or more candidate constraint sequences.
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a Service (SaaS) featuring software for use in
creating, rendering, executing and displaying animation,
visual effects, video and computer games, and digital media
content; computer software design; computer graphic design;
computer consulting services regarding computer graphic
design and digital media content.
92.
EVEN OUT WEARING OF MACHINE COMPONENTS DURING MACHINING
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design and manufacture of physical structures using subtractive manufacturing systems and techniques include, in one aspect, a method including obtaining information regarding a geometry of a part to be machined by a computer-controlled manufacturing system from a workpiece; based on the information regarding the geometry, identifying machine components to be used by the computer-controlled manufacturing system during machining the part; determining a position for the machining of the part with respect to at least one of the machine components, to even out wear on the machine components, based on data indicating previous positions, movements and wear of components associated with the computer-controlled manufacturing system; and providing instructions usable by the computer-controlled manufacturing system, wherein the instructions are configured to cause the computer-controlled manufacturing system to use the position for the machining.
B23Q 17/09 - Agencements sur les machines-outils pour indiquer ou mesurer pour indiquer ou mesurer la pression de coupe ou l'état de l'outil de coupe, p. ex. aptitude à la coupe, charge sur l'outil
B23Q 17/10 - Agencements sur les machines-outils pour indiquer ou mesurer pour indiquer ou mesurer la vitesse de coupe ou le nombre de révolutions
B23Q 17/20 - Agencements sur les machines-outils pour indiquer ou mesurer pour indiquer ou mesurer les caractéristiques de la pièce, p. ex. contour, dimensions, dureté
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of structures include, in one aspect, a method for inferring a blending function. A control mesh for a T-spline surface is obtained. A blending function mesh for inferring the blending function is generated for a control point of the T-spline surface by defining a topology for the blending function mesh. Defining the topology for the blending function mesh comprises: defining a central vertex and central edges of the blending function mesh that are inferred from the control mesh, and generating further topology for the blending function mesh by directly inferring further faces and edges for the blending function mesh from the defined central edges of the blending function mesh. The blending function for the T-spline surface can be inferred from the generated blending function mesh and provided providing for use for computing the T-spline surface.
G06T 17/30 - Description de surfaces, p. ex. description de surfaces polynomiales
G06F 30/12 - CAO géométrique caractérisée par des moyens d’entrée spécialement adaptés à la CAO, p. ex. interfaces utilisateur graphiques [UIG] spécialement adaptées à la CAO
G06F 30/23 - Optimisation, vérification ou simulation de l’objet conçu utilisant les méthodes des éléments finis [MEF] ou les méthodes à différences finies [MDF]
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Software as a service (SAAS) featuring software for digital animation, three-dimensional character performance and animation, graphics, special effects, visual effects, video and computer games for use in the field of entertainment; software as a service (SAAS) featuring software in the nature of cloud-based integrated workflows and workflow environments, data and technology services between computer software programs for creating, collaborating, developing, rendering, manipulating, executing, viewing, and displaying digital images and photographs, digital animation, three-dimensional character performance and animation, graphics, special effects, visual effects, video and computer games for use in the field of entertainment; software as a service (SAAS) services featuring software using artificial intelligence for enabling users to animate, light, and compose CG (computer generated) 3D characters into videos and 3D environments; providing on-line non-downloadable software using artificial intelligence for enabling users to animate, light, and compose CG (computer generated) 3D characters into videos and 3D environments via a website; providing temporary use of on-line non-downloadable cloud computing software using artificial intelligence for enabling users to animate, light, and compose CG (computer generated) 3D characters into videos and 3D environments.
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Software as a service (SAAS) featuring software for digital animation, three-dimensional character performance and animation, graphics, special effects, visual effects, video and computer games for use in the field of entertainment; Software as a service (SAAS) featuring software in the nature of cloud-based integrated workflows and workflow environments, data and technology services between computer software programs for creating, collaborating, developing, rendering, manipulating, executing, viewing, and displaying digital images and photographs, digital animation, three-dimensional character performance and animation, graphics, special effects, visual effects, video and computer games for use in the field of entertainment; Software as a service (SAAS) services featuring software using artificial intelligence for enabling users to animate, light, and compose CG (computer generated) 3D characters into videos and 3D environments; providing a website featuring on-line non-downloadable software using artificial intelligence for enabling users to animate, light, and compose CG (computer generated) 3D characters into videos and 3D environments; providing temporary use of on-line non-downloadable cloud computing software using artificial intelligence for enabling users to animate, light, and compose CG (computer generated) 3D characters into videos and 3D environments
A method and system provide the ability to track object progress in a drawing sheet. An object type is created and activity types are assigned to the object type. The activity types represent a progression of an object of the object type. A drawing sheet, without computer aided design (CAD) or building information model (BIM) context, that has multiple symbol instances, is obtained. A symbol instance is selected in the drawing sheet. A markup is created on the drawing sheet based on the symbol instance. Multiple symbol instances are autonomously detected based on the selected symbol instance. Progress tracking markup instances of the markup are autonomously created for the detected symbol instances and are linked to the object type. The progress of the object instances is tracked based on the markups.
Techniques are disclosed for generating training datasets and training generative artificial intelligence (AI) models for mechanical assembly designs. A method includes receiving a catalog of mechanical parts and generating a parts grammar that defines compatibility relationships between the parts. Using the parts grammar, one or more combined mechanical assemblies are generated, each comprising compatible mechanical parts. Assembly metrics are then generated by applying one or more physics simulations to the combined mechanical assemblies. A dataset is created based on the assemblies and corresponding assembly metrics, and used to train a generative AI model. Training includes executing an iterative training process in which assembly metrics are provided as input to the generative AI model to generate predicted assemblies, comparing the predicted assemblies to ground truth assemblies to compute a transformer loss and a complexity loss, and updating model weights based on an aggregated loss metric until a convergence threshold is satisfied.
A computer-implemented method is disclosed for generating mechanical assemblies using iterative optimization and generative artificial intelligence (AI). The method includes receiving a mechanical parts catalog and assembly requirements, and executing an iterative generation process. The process comprises generating, via limited sampling, at least one combined mechanical assembly that may satisfy the requirements; generating, via a generative AI model, at least one complete mechanical assembly based on the combined assembly and the requirements; and generating assembly metrics by applying at least one physics simulation to the complete assembly. A reward score is generated based on the assembly metrics, and the iterative generation process is repeated based on the reward score until a convergence threshold is satisfied. The method further includes performing at least one operation associated with the complete mechanical assembly.
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
Techniques are disclosed for generating training datasets and training generative artificial intelligence (AI) models for mechanical assembly designs. A method includes receiving a catalog of mechanical parts and generating a parts grammar that defines compatibility relationships between the parts. Using the parts grammar, one or more combined mechanical assemblies are generated, each comprising compatible mechanical parts. Assembly metrics are then generated by applying one or more physics simulations to the combined mechanical assemblies. A dataset is created based on the assemblies and corresponding assembly metrics, and used to train a generative AI model. Training includes executing an iterative training process in which assembly metrics are provided as input to the generative AI model to generate predicted assemblies, comparing the predicted assemblies to ground truth assemblies to compute a transformer loss and a complexity loss, and updating model weights based on an aggregated loss metric until a convergence threshold is satisfied.
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
100.
TRAINING TRANSFORMER MODELS TO GENERATE MECHANICAL ASSEMBLIES
Techniques are disclosed for generating training datasets and training generative artificial intelligence (AI) models for mechanical assembly designs. A method includes receiving a catalog of mechanical parts and generating a parts grammar that defines compatibility relationships between the parts. Using the parts grammar, one or more combined mechanical assemblies are generated, each comprising compatible mechanical parts. Assembly metrics are then generated by applying one or more physics simulations to the combined mechanical assemblies. A dataset is created based on the assemblies and corresponding assembly metrics, and used to train a generative AI model. Training includes executing an iterative training process in which assembly metrics are provided as input to the generative AI model to generate predicted assemblies, comparing the predicted assemblies to ground truth assemblies to compute a transformer loss and a complexity loss, and updating model weights based on an aggregated loss metric until a convergence threshold is satisfied.
G06F 30/17 - Conception mécanique paramétrique ou variationnelle
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle
